ORIGINAL RESEARCH
Association of Fat Density With Subclinical Atherosclerosis
Nicholas J. Alvey, BA; Alison Pedley, PhD; Klara J. Rosenquist, MD; Joseph M. Massaro, PhD; Christopher J. O’Donnell, MD, MPH;
Udo Hoffmann, MD, MPH; Caroline S. Fox, MD, MPH
Background-—Ectopic fat density is associated with cardiovascular disease (CVD) risk factors above and beyond fat volume.
Volumetric measures of ectopic fat have been associated with CVD risk factors and subclinical atherosclerosis. The aim of this
study was to investigate the association between fat density and subclinical atherosclerosis.
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Methods and Results-—Participants were drawn from the Multi-Detector Computed Tomography (MDCT) substudy of the
Framingham Heart Study (n=3079; mean age, 50.1 years; 49.2% women). Fat density was indirectly estimated by computed
tomography attenuation (Hounsfield Units [HU]) on abdominal scan slices. Visceral fat (VAT), subcutaneous fat (SAT), and
pericardial fat HU and volumes were quantified using standard protocols; coronary and abdominal aortic calcium (CAC and AAC,
respectively) were measured radiographically. Multivariable-adjusted logistic regression models were used to evaluate the
association between adipose tissue HU and the presence of CAC and AAC. Overall, 17.1% of the participants had elevated CAC
(Agatston score [AS]>100), and 23.3% had elevated AAC (AS>age-/sex-specific cutoffs). Per 5-unit decrement in VAT HU, the odds
ratio (OR) for elevated CAC was 0.76 (95% confidence interval [CI], 0.65 to 0.89; P=0.0005), even after adjustment for body mass
index or VAT volume. Results were similar for SAT HU. With decreasing VAT HU, we also observed an OR of 0.79 (95% CI, 0.67 to
0.92; P=0.004) for elevated AAC after multivariable adjustment. We found no significant associations between SAT HU and AAC.
There was no significant association between pericardial fat HU and either CAC or AAC.
Conclusions-—Lower VAT and SAT HU, indirect estimates of fat quality, are associated with a lower risk of subclinical
atherosclerosis. ( J Am Heart Assoc. 2014;3:e000788 doi: 10.1161/JAHA.114.000788)
Key Words: atherosclerosis • epidemiology • fat density • obesity
O
besity affects individuals worldwide, with an estimated
2.8 million related deaths in 2008.1 Adiposity has
been associated with a number of cardiovascular disease
From the Harvard Medical School, Boston, MA (N.J.A.); National Heart, Lung,
and Blood Institute’s Framingham Heart Study, Framingham, MA (N.J.A., A.P.,
K.J.R., C.J.O., C.S.F.); Division of Endocrinology and Metabolism, Brigham and
Women’s Hospital and Harvard Medical School, Boston, MA (K.J.R., C.S.F.);
NHLBI Division of Intramural Research and the Center for Population Studies,
Framingham, MA (K.J.R., C.S.F.); Department of Biostatistics, Boston University
School of Public Health, Boston, MA (J.M.M.); Cardiology Division, Departments
of Medicine (C.J.O.) and Radiology (U.H.), Massachusetts General Hospital and
Harvard Medical School, Boston, MA; NHLBI Division of Intramural Research,
Cardiovascular Epidemiology and Human Genomics Research, Bethesda, MD
(C.J.O.).
This article was handled independently by Viola Vaccarino, MD, PhD, as a
guest editor. The editors had no role in the evaluation of the manuscript or in
the decision about its acceptance.
Correspondence to: Caroline S. Fox, MD, MPH, 73 Mt Wayte Ave, Suite #2,
Framingham, MA 01702. E-mail: [email protected]
Received January 7, 2014; accepted July 23, 2014.
ª 2014 The Authors. Published on behalf of the American Heart Association,
Inc., by Wiley Blackwell. This is an open access article under the terms of the
Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is
properly cited and is not used for commercial purposes.
DOI: 10.1161/JAHA.114.000788
(CVD) risk factors, including hypertension (HTN) and
diabetes mellitus.2–6 Beyond generalized adiposity, different
fat depots confer varying degrees of CVD risk. For example,
larger visceral adipose tissue (VAT) volume has a stronger
adverse CVD risk profile than subcutaneous adipose tissue
(SAT).6–9
Although many studies have investigated risk profiles
based on absolute fat quantity, fat quality may also play an
important role in conferring CVD risk. Molecular and cellular
characteristics of adipose tissue, such as adipocyte size,10–13
reduced oxygenation,14,15 and dysfunctional inflammatory
response,16–18 are associated with adverse metabolic risk in
both animal models and humans. Radiographic imaging may
provide a noninvasive alternative for fat quality measurements. Computed tomography (CT) attenuation, measured in
Hounsfield Units (HU), is a quantitative measure of radiodensity to differentiate tissue types with the range of 195 to
45 HU that is attributed to adipose tissue.19 We have
recently shown that lower CT attenuation was associated with
higher CVD risk in both men and women, independent of fat
depot volumes.20
Fat volume is adversely associated with vascular calcification, a marker for atherosclerotic burden and a predictor
Journal of the American Heart Association
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Fat Density and Atherosclerosis
Alvey et al
Methods
Study Sample
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Participants were drawn from the Multi-Detector Computed Tomography (MDCT) substudy from the offspring
and third-generation cohorts of the Framingham Heart
Study, which has been described previously.20,29–32 Briefly,
between June 2002 and April 2005, this substudy enrolled
a total of 3394 participants, of which 3079 (1516 women)
were eligible for the study after exclusion for missing
outcome and risk factor/covariate data and current CVD.
For the pericardial fat analysis, a subset of 1120
individuals (621 women), who had complete data on
pericardial fat volume and CT attenuation, were drawn
from this overall sample.
Institutional review board approval was obtained from both
the Boston University Medical Center (Boston, MA) and
Massachusetts General Hospital (Boston, MA). All participants
provided written informed consent.
Measurement of Fat Volumes and Density
Each participant received supine 8-slice MDCT scans of the
abdomen and chest for VAT, SAT, and pericardial fat and
calcification measurements, as previously described (LightSpeed Ultra; General Electric, Milwaukee, WI).19 In the
abdomen, 25 contiguous 5-mm slices were imaged. A
dedicated offline workstation (Aquarius; Terarecon, San
Mateo, CA) was used for all radiographic measurements. Fat
was defined by CT attenuation as any pixel between 195
and 45 HU. VAT and SAT volumes and CT attenuation values
were measured by manually tracing the abdominal wall
separating the VAT and SAT depots. In the chest, scans
averaging 48 contiguous 2.5-mm slices of the heart were
taken. Pericardial fat was defined as any adipose tissue
located within the pericardial sac, and fat volume and CT
attenuation were measured by manual tracing. This technique
has produced high inter- and intrareader correlation in
previous work.19
DOI: 10.1161/JAHA.114.000788
Measurement of Calcium
Chest MDCT images were captured with a CT scanning
protocol that was prospectively triggered by ECG readings,
which allowed images to be taken at 70% of the cardiac
cycle.28 This procedure successfully captured nearly motionfree images of the coronary arteries. CAC lesions were
defined as any 3 consecutive pixels located in the coronary
arteries with an HU value of greater than 130 units. AAC
lesions were defined similarly in the abdominal images.
Calcium deposits were then scored according to the Agatston
scoring system. The presence of CAC was defined as an
Agatston score (AS) greater than 100, and the presence of
AAC was defined by age- and gender-specific 90th percentile
cutoffs.33 These threshholds were used in order to be
consistent with the existing literature. This protocol has also
produced high inter- and intrareader correlation in previous
work.28
Metabolic Risk Factors and Covariates
Risk factor data were originally obtained at offspring examination cycle 7 or the first examination of the third generation.
Body mass index (BMI) was defined as the weight in kilograms
divided by the height in meters squared. Waist circumference
was measured at the umbilicus using a tape measure. Serum
metabolic measures, including total and high-density lipoproteins, triglycerides (TGs), and glucose, were measured from
participants’ fasting samples. Current smokers were defined
as those who smoked, on average, ≥1 cigarette per day for
the past year. Physician-administered questions were used to
quantify alcohol use, and drinks/week were dichotomized and
stratified by gender using the following criteria: >14 drinks
per week in men or >7 drinks per week in women. Diabetes
was defined as fasting plasma glucose of ≥126 mg/dL or
current treatment with either a hypoglycemic agent or insulin.
Statistical Analysis
TGs were log transformed for normalization. Similarly, CAC
and AAC were log transformed after adding 1 unit to CAC
and AAC (log[CAC+1]) and log[AAC+1]), respectively. Ageadjusted Pearson’s correlation coefficients were computed to
assess the association between pericardial fat HU and various
continuous CVD risk factors. The Pearson’s correlation
coefficients were also calculated between log-transformed
CAC and AAC and adiposity measures for each fat depot
including HU.
Multivariable-adjusted logistic regression models were
constructed to assess the association between VAT and
SAT HU with the presence of CAC (AS>100) and AAC
(AS>age- and sex-specific cutoffs). For each outcome,
Journal of the American Heart Association
2
ORIGINAL RESEARCH
of future coronary events.21–28 However, less is known
about the association of fat quality with subclinical atherosclerosis. Thus, the aim of this study was to investigate
whether lower fat attenuation is associated with the
presence of coronary and abdominal aortic calcium (CAC
and AAC, respectively) above and beyond other CVD risk
factors and fat depot volumes. Because we have previously
shown that lower fat CT attenuation is associated with moreadverse CVD risk factors, we hypothesized that lower fat CT
attenuation would be associated with a higher odds of
subclinical atherosclerosis.
Alvey et al
Fat Density and Atherosclerosis
associated with increased calcium risk. Model 1 adjusted
for age and sex, and model 2 adjusted for age, sex, lipid
treatment, HTN treatment, smoking status, systolic blood
pressure (SBP), diabetes, total/high-density lipoprotein (HDL)
cholesterol, and (log-transformed) TG. Models 3, 4, and 5
included all covariates of model 2 with the following additional
adjustments: Model 3 included BMI, model 4 included the
corresponding fat volume (ie, the models for SAT HU were
adjusted for SAT volume), and model 5 included the other fat
Table 1. Characteristics of the Study Sample
Age, y
Overall (n=3079)
Women (n=1516)
Men (n=1563)
50.1 (9.9)
51.6 (9.5)
48.7 (10.1)
Smoking, %
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Never
49.1 (1512)
45.2 (685)
52.9 (827)
Former
38.1 (1174)
42.3 (641)
34.1 (533)
Current
12.8 (393)
12.5 (190)
13.0 (203)
Moderate alcohol use*, %
15.4 (473)
14.9 (226)
15.8 (247)
Total cholesterol, mg/dL
197 (35)
198 (36)
196 (34)
HDL cholesterol, mg/dL
54 (17)
61 (17)
46 (12)
Total: HDL cholesterol, mg/dL
4.0 (1.4)
3.4 (1.1)
4.5 (1.4)
TGs, mg/dL
†
102 (71 to 153)
112 (75 to 171)
93 (66 to 137)
Fasting glucose, mg/dL
98 (19)
95 (17)
101 (20)
Postmenopausal, %
N/A
48.9 (741)
N/A
Hormone replacement, %
N/A
19.4 (290)
N/A
Diabetes, %
5.4 (165)
4.7 (71)
6.0 (94)
Hypertensive treatment, %
16.6 (512)
17.0 (258)
16.3 (254)
Lipid treatment, %
11.4 (351)
9.2 (140)
13.5 (211)
Systolic blood pressure, mm Hg
121 (16)
120 (18)
123 (14)
Diastolic blood pressure, mm Hg
76 (9)
74 (9)
78 (9)
17.1 (527)
10.8 (164)
23.2 (363)
CAC AS>100, %
‡
AAC AS>age-/sex-specific cutoffs , %
23.3 (716)
24.5 (371)
22.1 (345)
BMI, kg/m2
27.7 (5.2)
27.0 (5.8)
28.3 (4.4)
Waist circumference, cm
97 (14)
93 (15)
100 (12)
1759 (994)
1340 (828)
2166 (971)
93.9 (4.6)
92.4 (4.4)
95.2 (4.5)
2878 (1397)
3154 (1523)
2611 (1204)
101.0 (5.0)
102.3 (5.1)
99.6 (4.4)
121.1 (48.0)
109.3 (40.7)
135.7 (52.2)
94.4 (3.0)
95.0 (3.1)
93.7 (2.6)
3
VAT, cm
VAT HU
SAT, cm
3
SAT HU
Pericardial fat, cm
3§
Pericardial fat HU§
Data presented as mean (SD) for continuous characteristics or percentage (count) for categorical characteristics. AAC indicates abdominal aortic calcium; AS, Agatston score; BMI, body
mass index; CAC, coronary artery calcium; HDL, high-density lipoprotein; HU, Hounsfield Units; N/A, not available; SAT, subcutaneous adipose tissue; TGs, triglycerides; VAT, visceral
adipose tissue.
*Defined as >7 drinks/week (women) or >14 drinks/week (men).
†
Presented as median (25th to 75th quartiles).
‡
AAC AS age-/sex-specific cutoffs: men: 7 (<45 years old), 231 (45 to 54), 1922 (55 to 64), 4914 (65 to 74), and 8177 (≥75); women: 0 (<45 years old), 73 (45 to 54), 946 (55 to 64),
2263 (65 to 74), and 5742 (≥75).34
§
Pericardial fat sample counts: 1120 (overall), 621 (women), and 499 (men).
DOI: 10.1161/JAHA.114.000788
Journal of the American Heart Association
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ORIGINAL RESEARCH
5 models were constructed and the corresponding odds ratio
(OR) for a 5-unit decrease in VAT or SAT HU was calculated.
We opted to standardize our data to a 5-unit decrease in HU
because this is nearly 1 SD across all measurements. This is
analogous to using a continuous scale in that the P value is
identical. By standardizing the data, we are able to present it
in a way that is more clinically meaningful. Furthermore, the
HU data were modeled per decrement to be consistent with
our a priori hypothesis that lower HU levels would be
Fat Density and Atherosclerosis
Alvey et al
Overall (n=1120)
Age
0.01
Systolic blood pressure
0.10‡
Diastolic blood pressure
0.15‡
Glucose
0.08†
Total cholesterol
0.03
HDL cholesterol
0.11‡
Total: HDL cholesterol
Results
Study Sample Characteristics
0.07*
Log TGs
0.03
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Table 1 presents characteristics of the study cohort
(n=3079). Mean age was 50 years, and approximately half
of the participants (49.2%; n=1516) were women. Approximately 17% of the participants had CAC present (AS>100) and
23% had AAC present (AS>age-/sex-specific cut-offs). Overall
mean and SD of SAT HU was 101.0 and 5.0, VAT HU was
93.9 and 4.6, and pericardial fat HU was 94.4 and 3.0.
‡
BMI
0.20
Waist circumference
0.22‡
VAT (volume)
0.10‡
SAT (volume)
0.11‡
0.15‡
Pericardial fat (volume)
CAC
0.07*
AAC
0.02
VAT HU
0.05
SAT
0.05
Pearson’s Correlation Coefficients
Pericardial fat HU was directly correlated with most CVD risk
factors in the overall cohort (Table 2). For example, pericardial fat HU was directly correlated with both SBP (r=0.10;
P<0.001) and BMI (r=0.20; P<0.001). We also observed direct
correlations overall between pericardial fat HU and SAT
volume (r=0.11; P<0.001) and VAT volume (r=0.10; P<0.01).
However, there was an inverse correlation between pericardial
fat HU and pericardial fat volume (r= 0.15; P<0.001).
Table 3 presents correlation coefficients between measures of adiposity and calcium. For example, BMI was directly
correlated with CAC (r=0.13; P<0.001). CAC was directly
CAC and AAC presented as log (CAC+1) and log (AAC+1), respectively. AAC indicates
abdominal aortic calcium; BMI, body mass index; CAC, coronary artery calcium;
HDL, high-density lipoprotein; HU, Hounsfield Units; SAT, subcutaneous adipose tissue;
TGs, triglycerides; VAT, visceral adipose tissue.
*P<0.05; †P<0.01; ‡P<0.001.
depot density measure (ie, the models for SAT HU were
adjusted for VAT HU). Five multivariable-adjusted logistic
regression models were also constructed to assess the
association between a 5-unit decrease in pericardial fat HU
Table 3. Age-Adjusted Pearson’s Correlation Coefficients Between Adiposity Measures and Coronary and Abdominal Aortic
Calcium Presence
Overall
CAC
AAC
‡
BMI
Women
0.13
‡
0.14
CAC
‡
‡
Waist circumference
0.17
0.17
VAT (volume)
0.25‡
0.24‡
VAT HU
0.10
SAT (volume)
0.02
0.14‡
SAT HU
Pericardial fat (volume)
§
Pericardial fat HU§
‡
0.24
0.07*
0.13‡
0.05
†
0.02
0.18
AAC
0.04
0.11
0.02
CAC
‡
‡
0.05
0.13
0.07†
0.17‡
0.002
0.02
0.13
0.01
0.13‡
‡
0.12
0.14‡
0.12‡
0.18‡
0.14
0.002
‡
‡
0.05*
†
AAC
‡
0.09‡
0.09
0.04
‡
Men
0.12
†
0.02
0.09
0.04
‡
0.16
0.03
0.07†
0.09‡
0.01
0.18‡
0.10*
CAC and AAC presented as log(CAC+1) and log(AAC+1), respectively. AAC indicates abdominal aortic calcium; BMI, body mass index; CAC, coronary artery calcium; HU, Hounsfield Units;
SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.
*P<0.05; †P<0.01; ‡P<0.001; §Pericardial fat sample counts: 1120 (overall), 621 (women), and 499 (men).
DOI: 10.1161/JAHA.114.000788
Journal of the American Heart Association
4
ORIGINAL RESEARCH
and the presence of CAC and AAC. The first 3 models
adjusted for the same covariates as models 1, 2, and 3 above.
The fourth model adjusted for VAT volume, and the fifth model
adjusted for pericardial fat volume.
SAS statistical software (version 9.2; SAS Institute, Cary,
NC) was used for all analyses. P<0.05 was considered
statistically significant.
Table 2. Age-Adjusted Pearson’s Correlation Coefficients
Between Cardiovascular Risk Factors and Pericardial Fat HU
Fat Density and Atherosclerosis
Alvey et al
CAC>100
P Value*
AAC>Age-/Sex-Specific Cutoffs†
P Value*
1.02 (0.89 to 1.16)
0.81
1.18 (1.07 to 1.30)
0.0007
VAT HU
Age, gender adjusted
Multivariable adjusted
‡
0.76 (0.65 to 0.89)
0.0005
0.90 (0.80 to 1.02)
0.09
Multivariable+BMI adjusted
0.71 (0.61 to 0.84)
<0.0001
0.90 (0.79 to 1.02)
0.09
Multivariable+VAT adjusted
0.60 (0.49 to 0.74)
<0.0001
0.79 (0.67 to 0.92)
0.004
Multivariable+SAT HU adjusted
0.83 (0.70 to 0.99)
0.04
0.92 (0.81 to 1.05)
0.24
0.87 (0.77 to 0.99)
0.03
1.03 (0.94 to 1.13)
0.53
SAT HU
Age, gender adjusted
Multivariable adjusted
‡
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0.79 (0.69 to 0.90)
0.0004
0.93 (0.84 to 1.03)
0.15
Multivariable+BMI adjusted
0.76 (0.67 to 0.88)
0.0001
0.93 (0.83 to 1.03)
0.16
Multivariable+SAT adjusted
0.71 (0.61 to 0.83)
<0.0001
0.94 (0.84 to 1.05)
0.29
Multivariable+VAT HU adjusted
0.85 (0.73 to 0.99)
0.04
0.96 (0.85 to 1.08)
0.47
1.02 (0.79 to 1.31)
0.89
1.14 (0.92 to 1.41)
0.24
0.97 (0.74 to 1.27)
0.82
1.10 (0.87 to 1.38)
0.44
Multivariable+BMI adjusted
0.98 (0.75 to 1.29)
0.89
1.07 (0.84 to 1.36)
0.58
Multivariable+VAT adjusted
0.97 (0.74 to 1.27)
0.85
1.10 (0.87 to 1.38)
0.44
Multivariable+pericardial fat adjusted
0.93 (0.70 to 1.23)
0.60
1.11 (0.88 to 1.42)
0.38
Pericardial fat HU
Age, gender adjusted
Multivariable adjusted
‡
Estimates for HU are given as odds ratio (95% confidence intervals). AAC indicates abdominal aortic calcium, BMI, body mass index; CAC, coronary artery calcium; HU, Hounsfield Units;
SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.
*P values for sex interaction for CAC: VAT HU (P=0.90); SAT HU (P=0.63); and pericardial fat HU (P=0.81). P values for sex interaction for AAC: VAT HU (P=0.82); SAT HU (P=0.83); and
pericardial fat HU (P=0.17). P values for age interaction for CAC: VAT HU (P=0.73); SAT HU (P=0.17); and pericardial fat HU (P=0.94). P values for age interaction for AAC: VAT HU
(P=0.38); SAT HU (P=0.47); and pericardial fat HU (P=0.47).
†
AAC Agatston score age-/sex-specific cutoffs: men: 7 (<45 years old), 231 (45 to 54), 1922 (55 to 64), 4914 (65 to 74), and 8177 (≥75); women: 0 (<45 years old), 73 (45 to 54), 946 (55
to 64), 2263 (65 to 74), and 5742 (≥75).
‡
Adjusted for age, gender, lipid treatment, hypertension treatment, smoking status, systolic blood pressure, diabetes, total/high-density lipoprotein cholesterol ratio, and triglycerides.
correlated with SAT HU (r=0.14; P<0.001) and pericardial HU
(r=0.07; P<0.05), but was not associated with VAT HU. AAC
was inversely correlated with VAT HU (r= 0.13; P<0.001),
but not with SAT HU or pericardial fat HU.
CAC and AAC Risk
Multivariable-adjusted associations between fat density and
calcium are presented in Table 4. Contrary to our a priori
hypothesis, per 5-unit decrement in VAT HU, we observed an
OR of 0.76 for CAC (P=0.0005) in the multivariable-adjusted
model. This association persisted after adjustment for BMI
(OR, 0.71; P<0.0001), VAT volume (OR, 0.60; P<0.0001), and
SAT HU (OR, 0.83; P=0.04). For SAT HU, we observed an OR
of 0.79 for CAC per 5-unit decrement in SAT HU (P=0.0004) in
the multivariable model. Similarly, this association persisted
after adjustment for BMI (OR, 0.76; P=0.0001), SAT volume
(OR, 0.71; P<0.0001), and VAT HU (OR, 0.85; P=0.04).
For VAT HU and AAC, there was an 18% higher risk of
calcification in the age-sex adjusted model (P<0.001). This
DOI: 10.1161/JAHA.114.000788
association was nonsignificant after multivariable adjustment
(OR, 0.9; P=0.09) and slightly stronger after additional
adjustment for VAT volume (OR, 0.79 for AAC; P=0.004).
We observed no significant association between SAT HU and
AAC in the multivariable or serial fat-depot adjusted models.
Furthermore, we did not find significant associations between
pericardial fat HU and either CAC or AAC in any of the models
performed. Associations did not differ by sex (all P≥0.17).
Discussion
Our principal findings are 3-fold. Contrary to our a priori
hypothesis, we observed that lower VAT and SAT HU were
associated with a lower OR for CAC. These associations
remained after further adjustment for CVD risk factors as well
as VAT or SAT volumes. Similarly, lower VAT HU was associated
with lower OR for AAC. Finally, we observed no association with
pericardial fat HU and the presence of either CAC or AAC.
Taken together, these findings suggest that indices of abdominal fat density are associated with subclinical atherosclerosis.
Journal of the American Heart Association
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ORIGINAL RESEARCH
Table 4. Multivariable-Adjusted Logistic Regression for CAC and AAC With Cardiovascular Risk Factors by VAT HU, SAT HU, and
Pericardial Fat HU Per 5 Unit Decrement in VAT, SAT, or Pericardial Fat HU
Fat Density and Atherosclerosis
Alvey et al
DOI: 10.1161/JAHA.114.000788
state43–46 and the development of atherosclerosis.47 Therefore, more-fibrotic adipose tissue, characterized as lessnegative HU values, would be associated with higher odds of
vascular calcification, consistent with the results of the
present study.
Additionally, adipose-tissue–specific hormones, such as
adiponectin and leptin,48,49 may also mediate the association
between obesity, fibrosis, and subclinical atherosclerosis. Low
serum levels of adiponectin and/or high serum levels of leptin
have fibrogenic and vascular calcification effects50–53 and
could mediate an association between adipose fibrosis and
subclinical atherosclerosis. In published work, higher adipose
tissue density was associated with lower leptin and higher
adiponectin values.54 Taken together, there are several
potential mechanisms that may explain our findings of the
association between abdominal fat density and subclinical
atherosclerosis. One potential reason for our disparate
findings is that the cross-sectional nature of our work does
not allow us to classify the temporal nature of disease
exposure and outcome. CVD risk factors occur before the
onset of subclinical atherosclerosis, and it is possible that we
are picking up relationships at different points in time. Next,
the histological correlates of fat density are uncertain and
likely represent multiple different cellular mechanisms. The
relative contribution of each component remains uncertain.
Ultimately, the disparate findings that we have uncovered,
relative to our initial hypothesis, might provide further insights
into the relationships between fat quality and subclinical
atherosclerosis. Further work, using longitudinal samples, will
be necessary to better clarify these associations.
Our current observations indicate the importance of better
understanding risks related to abdominal fat density above
and beyond fat volume. Though CT imaging provides an
indirect, noninvasive marker of fat density, the underlying
molecular and structural characterization of varying CT
attenuation still requires elucidation. Additionally, further
investigation should focus on providing mechanistic insight
for the association between abdominal fat density and
subclinical atherosclerosis. For example, investigating the
association between abdominal fat density and adiposetissue–derived inflammatory factors would help to clarify this
mechanism. Furthermore, associations between abdominal
fat density and cardiac procedures, such as coronary artery
bypass or stent implantation, could begin to elucidate the
effect fat density has on clinical outcomes. However, though
fat density estimation by CT imaging could provide CVD risk
prediction, such is beyond the scope of this current mechanistic investigation.
Strengths of this current study include the large sample
size, community-based design without enrichment for adiposity, and the use of CT imaging and reproducible protocols for
fat measurements. Limitations include the cross-sectional
Journal of the American Heart Association
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ORIGINAL RESEARCH
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We observed very few associations with pericardial fat HU,
suggesting that the anatomic location of fat density measurements may be important. We also cannot rule out that
differences in our chest, as compared to abdominal, radiographic protocol obscured our ability to identify meaningful
associations with pericardial fat HU and risk factors. Finally,
pericardial fat HU data were available in our offspring sample
only, which is, on average, older than the third-generation
sample. It is possible that any potential association is
attenuated in older individuals.
Multiple studies have investigated and established associations between obesity, adipose tissue volumes, and vascular
calcification. The Muscatine Study, a longitudinal cohort study
of 384 individuals, found that higher BMI during late childhood
and early adult life was associated with CAC presence in early
adult life.23 Using waist and hip girth measures, the CARDIA
study showed that abdominal obesity, including duration of
obesity,27 was associated with subclinical atherosclerosis in
young adults.26 Beyond anthropometric measures, the St.
Francis Heart Study used CT measures to demonstrate an
association between intraabdominal adiposity and the presence of CAC in adults aged 50 to 70.24 Similarly, our group
has previously identified an association between fat volumes
and subclinical atherosclerosis using multislice CT scans.28
Our current study advances the literature by using a
noninvasive technique to measure fat density. This study
builds on previous work from our group regarding the
association of abdominal fat density and metabolic risk,
where we showed that lower (ie, more-negative HU) was
associated with more-adverse metabolic and CVD risk,
including fat quantity.20
CT attenuation of adipose tissue may indicate a variety of
cellular and tissue characteristics. First, more-negative HU
values are associated with more lipid-dense fat tissue.35
Second, adipose tissue that is poorly vascularized is characterized by a more-negative HU value resulting from the
radiographic properties of blood.36 Third, fibrotic adipose
tissue is characterized by a less-negative HU value, compared
to nonfibrotic adipose tissue, as a result of higher tissue
density from excess collagen deposition. Considering these
characteristics in the context of the findings from our study,
our results are most consistent with adipose tissue fibrosis
explaining the association between fat attenuation and
vascular calcification.
Obesity is a chronic inflammatory condition.37,38 Dietinduced obesity causes adipocyte hypertrophy and hyperplasia from excess lipid accumulation.34 Adipocyte hypertrophy
and hyperplasia induces tissue hypoxia, because the rapid
adipose tissue expansion outpaces vascular growth,14 ultimately progressing to excess collagen deposition and fibrosis.39,40 Both tissue hypoxia and fibrosis result in adipocyte
necrosis,40–42 leading to a systemic chronic inflammatory
Fat Density and Atherosclerosis
Alvey et al
Conclusion
Abdominal fat density is associated with subclinical atherosclerosis. Our findings warrant further investigation into the
association between fat density and atherosclerosis.
Acknowledgments
distribution, and the metabolic syndrome in older men and women. Arch Intern
Med. 2005;165:777–783.
10. Salans LB, Knittle JL, Hirsch J. The role of adipose cell size and adipose tissue
insulin sensitivity in the carbohydrate intolerance of human obesity. J Clin
Invest. 1968;47:153–165.
11. Weyer C, Foley JE, Bogardus C, Tataranni PA, Pratley RE. Enlarged
subcutaneous abdominal adipocyte size, but not obesity itself, predicts type
II diabetes independent of insulin resistance. Diabetologia. 2000;43:1498–
1506.
12. Anand SS, Tarnopolsky MA, Rashid S, Schulze KM, Desai D, Mente A,
Rao S, Yusuf S, Gerstein HC, Sharma AM. Adipocyte hypertrophy, fatty
liver and metabolic risk factors in South Asians: the Molecular Study of
Health and Risk in Ethnic Groups (mol-SHARE). PLoS One. 2011;6:
e22112.
13. Yang J, Eliasson B, Smith U, Cushman SW, Sherman AS. The size of large
adipose cells is a predictor of insulin resistance in first-degree relatives of type
2 diabetic patients. Obesity (Silver Spring). 2012;20:932–938.
14. Pasarica M, Sereda OR, Redman LM, Albarado DC, Hymel DT, Roan LE, Rood
JC, Burk DH, Smith SR. Reduced adipose tissue oxygenation in human obesity:
evidence for rarefaction, macrophage chemotaxis, and inflammation without
an angiogenic response. Diabetes. 2009;58:718–725.
Downloaded from http://jaha.ahajournals.org/ by guest on June 18, 2017
This work was made possible through the use of resources and data
from the Framingham Heart Study of the National Heart, Lung and
Blood Institute (NHLBI) of the National Institutes of Health and
Boston University School of Medicine.
15. Yin J, Gao Z, He Q, Zhou D, Guo Z, Ye J. Role of hypoxia in obesity-induced
disorders of glucose and lipid metabolism in adipose tissue. Am J Physiol
Endocrinol Metab. 2009;296:E333–E342.
Sources of Funding
17. Cinti S, Mitchell G, Barbatelli G, Murano I, Ceresi E, Faloia E, Wang S, Fortier M,
Greenberg AS, Obin MS. Adipocyte death defines macrophage localization and
function in adipose tissue of obese mice and humans. J Lipid Res. 2005;
46:2347–2355.
This research was, in part, supported by the NHLBI’s FHS
(Contract No. N01-HC-25195). Dr Fox is supported by the
NHLBI Division of Intramural Research.
Disclosures
Dr Pedley is an employee of Merck and Co, Inc.
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Association of Fat Density With Subclinical Atherosclerosis
Nicholas J. Alvey, Alison Pedley, Klara J. Rosenquist, Joseph M. Massaro, Christopher J. O'Donnell,
Udo Hoffmann and Caroline S. Fox
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J Am Heart Assoc. 2014;3:e000788; originally published August 28, 2014;
doi: 10.1161/JAHA.114.000788
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